Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer

Radiation Oncology

Katia Parodi¹  Minglun Li²  Maria Kawula²  Dinu Purice³  Christopher Kurz³  Guillaume Landry³  Claus Belka⁴  Gerome Vivar⁵  Seyed-Ahmad Ahmadi⁵ 

[1] Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany;Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany;Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany;Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany;Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany;German Cancer Consortium (DKTK), Munich, Germany;German Center for Vertigo and Balance Disorders, Ludwig-Maximilians-Universität München, Planegg, Germany;

关键词: 3D U-Net;    Automatic segmentation;    Radiation therapy;    Prostate cancer;    Neural networks;    Deep learning;   

DOI  :  10.1186/s13014-022-01985-9

来源: Springer

【 授权许可】

CC BY